Advances in imaging techniques have lead to early detection of tumors but have had small (approximately 15-30%) impact on those malignant tumor types currently responsible for most patient mortality including lung and breast cancer. Existing techniques fail to provide quantitative and objective metrics to predict which suspect detected nodules would be found malignant if biopsied. For example, standard mammography method relies heavily on the subjective, experience, and non-quantitative judgment of highly trained mammographic radiologists. Specifically, the detection and diagnosis is based on a radiologist visually reading and interpreting two projection X-ray radiographs in the cranio-caudal (CC) and medial-lateral-oblique (MLO) orientations taken with breast compression. In addition, although some improvements have been made in existing techniques to detect smaller tumors, such improvements tend to worsen the problem of over-diagnosis, causing more harm than good. For example, by focusing on detecting the smaller tumors, more false positives (e.g., benign nodules) may also be detected, leading to more resources spent (e.g., performing additional testing or surgery) and potentially more penalties introduced (e.g., permanent loss of lung capacity due to the surgery).
Moreover, there is a growing realization that over-diagnosis and overtreatment of cancer diseases may be widespread, and a significant percentage (e.g., approximately 90%) of cancer patients who die of cancer actually die due to metastasis and not due to the lack of local control in treating the primary tumor(s). Existing techniques also fail to effectively and objectively identify, assess, and manage such malignancies that lead to fatality as well as those that do not due to indolent properties like slow, no or negative growth rates. Diagnosis and treatment of the latter contribute to over-diagnosis and overtreatment.